A third category focuses on ranking and/or detection [1]}, [2]}, [3]}.
Zeno in [2]} ranks each worker using a score which depends on the estimated loss reduction and the magnitude of the update. Zeno requires strict assumptions on the smoothness of the loss and the variance of the gradient estimates in order to tolerate an adversarial majority in the cluster. Similarly, ByGARS [3]} computes reputation scores for the computing nodes based on an auxiliary dataset; these scores are used to weigh the contribution of each gradient to the model update. Its convergence result depend on the assumption of a strongly convex loss. In contrast, our method does not rely on such restrictive assumptions.
